322,609 research outputs found
A two-base encoded DNA sequence alignment problem in computational biology
The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics. The primary objective of the "sequence alignment" problem is to search for a new algorithm that facilitates the use of two-base encoded data for large-scale re-sequencing projects. This algorithm should be able to perform local sequence alignment as well as error detection and correction in a reliable and systematic manner, enabling the direct comparison of encoded DNA sequence reads to a candidate reference DNA sequence.
We will first briefly review two well-known sequence alignment approaches and provide a rudimentary improvement for implementation on parallel systems. Then, we carefully examin a unique sequencing technique known as the SOLiDTM System that can be implemented, and follow by the results from the global and local sequence alignment.
In this report, the team presents an explanation of the algorithms for color space sequence data from the high-throughput re-sequencing technology and a theoretical parallel approach to the dynamic programming method for global and local alignment. The combination of the di-base approach and dynamic programming provides a possible viewpoint for large-scale re-sequencing projects. We anticipate the use of distributed computing to be the next-generation engine for large-scale problems like such
A Two-Phase Dynamic Programming Algorithm Tool for DNA Sequences
Sequence alignment has to do with the arrangement of DNA, RNA, and protein sequences to identify areas of similarity. Technic ally, it
involves the arrangement of the primary sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of
functional, structural, or evolutionary relationships between the sequences. Similarity may be a consequence of functional, s tructural, or
evolutionary relationships between the sequences. If two sequences in an alignment share a common ancestor, mismatches can be
interpreted as mutations, and gaps as insertions. Such information becomes of great use in vital areas such as the study of d iseases,
genomics and generally in the biological sciences. Thus, sequence alignment presents not just an exciting field of study, but a field of
great importance to mankind. In this light, we extensively studied about seventy (70) existing sequence alignment tools available to us.
Most of these tools are not user friendly and cannot be used by biologists. The few tools that attempted both Local and Global algorithms
are not ready available freely. We therefore implemented a sequence alignment tool (CU-Aligner) in an understandable, user-friendly and
portable way, with click-of-a-button simplicity. This is done utilizing the Needleman-Wunsh and Smith-Waterman algorithms for global
and local alignments, respectively which focuses primarily on DNA sequences. Our aligner is implemented in the Java language in both
application and applet mode and has been efficient on all windows operating systems
An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.
Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks
MAVID: Constrained ancestral alignment of multiple sequences
We describe a new global multiple alignment program capable of aligning a
large number of genomic regions. Our progressive alignment approach
incorporates the following ideas: maximum-likelihood inference of ancestral
sequences, automatic guide-tree construction, protein based anchoring of
ab-initio gene predictions, and constraints derived from a global homology map
of the sequences. We have implemented these ideas in the MAVID program, which
is able to accurately align multiple genomic regions up to megabases long.
MAVID is able to effectively align divergent sequences, as well as incomplete
unfinished sequences. We demonstrate the capabilities of the program on the
benchmark CFTR region which consists of 1.8Mb of human sequence and 20
orthologous regions in marsupials, birds, fish, and mammals. Finally, we
describe two large MAVID alignments: an alignment of all the available HIV
genomes and a multiple alignment of the entire human, mouse and rat genomes
Local structural alignment of RNA with affine gap model
BACKGROUND: Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps. RESULTS: In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem. CONCLUSIONS: Based on an experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.published_or_final_versio
Global Alignment of Molecular Sequences via Ancestral State Reconstruction
Molecular phylogenetic techniques do not generally account for such common
evolutionary events as site insertions and deletions (known as indels). Instead
tree building algorithms and ancestral state inference procedures typically
rely on substitution-only models of sequence evolution. In practice these
methods are extended beyond this simplified setting with the use of heuristics
that produce global alignments of the input sequences--an important problem
which has no rigorous model-based solution. In this paper we consider a new
version of the multiple sequence alignment in the context of stochastic indel
models. More precisely, we introduce the following {\em trace reconstruction
problem on a tree} (TRPT): a binary sequence is broadcast through a tree
channel where we allow substitutions, deletions, and insertions; we seek to
reconstruct the original sequence from the sequences received at the leaves of
the tree. We give a recursive procedure for this problem with strong
reconstruction guarantees at low mutation rates, providing also an alignment of
the sequences at the leaves of the tree. The TRPT problem without indels has
been studied in previous work (Mossel 2004, Daskalakis et al. 2006) as a
bootstrapping step towards obtaining optimal phylogenetic reconstruction
methods. The present work sets up a framework for extending these works to
evolutionary models with indels
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